Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

中文翻译：

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传感器技术和决策支持智能工具的进步，助力智能畜牧业

在过去的 20 年里，远程监控、通过传感器进行的现代数据收集、快速的数据传输以及通过物联网 (IoT) 进行的大量数据存储推动了精准畜牧业 (PLF) 的发展。PLF 与畜牧生产的许多领域相关，包括基于空中和卫星的牧场草料数量和质量测量；体重和成分以及生理评估；用于监测放牧和觅食环境中的位置、活动和行为的动物设备；及早发现跛行和其他疾病；产奶量和成分；生殖测量和产犊疾病；以及采食量和温室气体排放，仅举几例。通过 PLF 改善动物生产有很多可能性，但是PLF和计算机建模的结合对于促进农场应用是必要的。概念或知识驱动（机械）模型建立在科学知识的基础上，它们基于对可变相互关系假设的概念化。另一方面，人工智能 (AI) 是一种数据驱动的方法，可以操纵和表示传感器和物联网积累的大数据。尽管如此，它仍无法明确解释数据核心中内在关系的基本假设，因为它缺乏赋予理解和原则的智慧。人工智能缺乏智慧是因为一切都围绕着数字。数字之间的关联是通过数学相关性和协方差的“自动化”学习过程获得的，不是通过“人类因果关系”和对生理或生产原理的抽象概念化。人工智能从比较类比开始建立概念，并为未来的比较提供记忆。然后，学习过程从寻求智慧演变为系统地使用推理。人工智能在许多科学领域都是一个相对新颖的概念。加快从传统的最大化产出心态转变为更加注重优化生产效率同时减轻生产资源配置的“缺失环节”很可能是“缺失的一环”。通过机械和人工智能模型的并行混合，概念和数据驱动建模之间的集成将产生一个混合智能机械模型，以及通过 PLF 收集数据，